Combining Alamouti STBC with Block Diagonalization for Downlink MU-MIMO System over Rician Channel for 5G

Wireless communication faces a number of adversities and obstacles as a result of fading and co-channel interference (CCI). Diversity with beamformer techniques may be used to mitigate degradation in the system performance. Alamouti space-time-block-code (STBC) is a strong scheme focused on accomplishing spatial diversity at the transmitter, which needs a straightforward linear processing in the receiver. Also, high bit-error-rate (BER) performance can be achieved by using the multiple-input multiple-output (MIMO) system with beamforming technology. This approach is particularly useful for CCI suppression. Exploiting the channel state information (CSI) at the transmitter can improve the STBC through the use of a beamforming precoding. In this paper, we propose the combination between Alamouti STBC and block diagonalization (BD) for downlink multi-user MIMO system. Also, this paper evaluates the system performance improvement of the extended Alamouti scheme, with the implementation of BD precoding over a Rayleigh and Rician channel. Simulation results show that the combined system has performance better than the performance of beamforming system. Also, it shows that the combined system performance of extended Alamouti outperforms the combined system performance without extended Alamouti. Furthermore, numerical results confirm that the Rician channel can significantly improve the combined system performance

Doctor of Philosophy

dissertationThe use of multicarrier techniques has allowed the rapid expansion of broadband wireless communications. Orthogonal frequency division multiplexing (OFDM) has been the most dominant technology in the past decade. It has been deployed in both indoor Wi-Fi and cellular environments, and has been researched for use in underwater acoustic channels. Recent works in wireless communications include the extension of OFDM to multiple access applications. Multiple access OFDM, or orthogonal frequency division multiple access (OFDMA), has been implemented in the third generation partnership project (3GPP) long- term evolution (LTE) downlink. In order to reduce the intercarrier interference (ICI) when user's synchronization is relaxed, filterbank multicarrier communication (FBMC) systems have been proposed. The first contribution made in this dissertation is a novel study of the classical FBMC systems that were presented in 1960s. We note that two distinct methods were presented then. We show that these methods are closely related through a modulation and a time/frequency scaling step. For cellular channels, OFDM also has the weakness of relatively large peak-to-average power ratios (PAPR). A special form of OFDM for the uplink of multiple access networks, called single carrier frequency division multiple access (SC-FDMA), has been developed to mitigate this issue. In this regard, this dissertation makes two contributions. First, we develop an optimization method for designing an effective precoding method for SC-FDMA systems. Second, we show how an equivalent to SC-FDMA can be developed for systems that are based on FBMC. In underwater acoustic communications applications, researchers are investigating the use of multicarrier communication systems like OFDM in underwater channels. The movement of the communicating vehicles scales the received signal along the time axis, which is often referred to as Doppler scaling. To undo the signal degradation, researchers have investigated methods to estimate the Doppler scaling factor and restore the original signal using resampling. We investigate a method called nonuniform fast Fourier transform (NUFFT) and apply that to increase the precision in the detection and correction of the Doppler scaling factor. NUFFT is applied to both OFDM and FBMC and its performance over the experimental data obtained from at sea experiments is investigated

Efficient low-complexity data detection for multiple-input multiple-output wireless communication systems

The tradeoff between the computational complexity and system performance in multipleinput multiple-output (MIMO) wireless communication systems is critical to practical applications. In this dissertation, we investigate efficient low-complexity data detection schemes from conventional small-scale to recent large-scale MIMO systems, with the targeted applications in terrestrial wireless communication systems, vehicular networks, and underwater acoustic communication systems. In the small-scale MIMO scenario, we study turbo equalization schemes for multipleinput multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) and multipleinput multiple-output single-carrier frequency division multiple access (MIMO SC-FDMA) systems. For the MIMO-OFDM system, we propose a soft-input soft-output sorted QR decomposition (SQRD) based turbo equalization scheme under imperfect channel estimation. We demonstrate the performance enhancement of the proposed scheme over the conventional minimum mean-square error (MMSE) based turbo equalization scheme in terms of system bit error rate (BER) and convergence performance. Furthermore, by jointly considering channel estimation error and the a priori information from the channel decoder, we develop low-complexity turbo equalization schemes conditioned on channel estimate for MIMO systems. Our proposed methods generalize the expressions used for MMSE and MMSE-SQRD based turbo equalizers, where the existing methods can be viewed as special cases. In addition, we extend the SQRD-based soft interference cancelation scheme to MIMO SC-FDMA systems where a multi-user MIMO scenario is considered. We show an improved system BER performance of the proposed turbo detection scheme over the conventional MMSE-based detection scheme. In the large-scale MIMO scenario, we focus on low-complexity detection schemes because computational complexity becomes critical issue for massive MIMO applications. We first propose an innovative approach of using the stair matrix in the development of massive MIMO detection schemes. We demonstrate the applicability of the stair matrix through the study of the convergence conditions. We then investigate the system performance and demonstrate that the convergence rate and the system BER are much improved over the diagonal matrix based approach with the same system configuration. We further investigate low-complexity and fast processing detection schemes for massive MIMO systems where a block diagonal matrix is utilized in the development. Using a parallel processing structure, the processing time can be much reduced. We investigate the convergence performance through both the probability that the convergence conditions are satisfied and the convergence rate, and evaluate the system performance in terms of computational complexity, system BER, and the overall processing time. Using our proposed approach, we extend the block Gauss-Seidel method to large-scale array signal detection in underwater acoustic (UWA) communications. By utilizing a recently proposed computational efficient statistic UWA channel model, we show that the proposed scheme can effectively approach the system performance of the original Gauss-Seidel method, but with much reduced processing delay

Análise de desempenho de recetores de baixa complexidade para mimo massivo em canais subaquáticos com correlação entre antenas.

O propósito da presente dissertação de Mestrado é demonstrar que os recetores de baixa complexidade, Equal Gain Combiner (EGC) e Maximum Gain Combiner (MRC), comparativamente a outros recetores como o Zero Forcing (ZF) que requer a inversão da matriz do canal para cada componente de frequência, apresentam a vantagem de diminuírem o processamento e complexidade de receção para um canal de comunicação acústica subaquática, tendendo, ainda, a apresentar um melhor desempenho. Esta dissertação mostra, ainda, que é possível mitigar os efeitos nefastos de um sistema correlacionado de múltiplas antenas de receção e transmissão, tradicionalmente designado por sistema Multiple Input Multiple Output (MIMO), que não pode ser não correlacionado devido ao insuficiente espaçamento entre antena

Compressive Sensing for Multi-channel and Large-scale MIMO Networks

Compressive sensing (CS) is a revolutionary theory that has important applications in many engineering areas. Using CS, sparse or compressible signals can be recovered from incoherent measurements with far fewer samples than the conventional Nyquist rate. In wireless communication problems where the sparsity structure of the signals and the channels can be explored and utilized, CS helps to significantly reduce the number of transmissions required to have an efficient and reliable data communication. The objective of this thesis is to study new methods of CS, both from theoretical and application perspectives, in various complex, multi-channel and large-scale wireless networks. Specifically, we explore new sparse signal and channel structures, and develop low-complexity CS-based algorithms to transmit and recover data over these networks more efficiently. Starting from the theory of sparse vector approximation based on CS, a compressive multiple-channel estimation (CMCE) method is developed to estimate multiple sparse channels simultaneously. CMCE provides a reduction in the required overhead for the estimation of multiple channels, and can be applied to estimate the composite channels of two-way relay channels (TWRCs) with sparse intersymbol interference (ISI). To improve end-to-end error performance of the networks, various iterative estimation and decoding schemes based on CS for ISI-TWRC are proposed, for both modes of cooperative relaying: Amplify-and-Forward (AF) and Decode-and-Forward (DF). Theoretical results including the Restricted Isometry Property (RIP) and low-coherent condition of the discrete pilot signaling matrix, the performance guarantees, and the convergence of the schemes are presented in this thesis. Numerical results suggest that the error performances of the system is significantly improved by the proposed CS-based methods, thanks to the awareness of the sparsity feature of the channels. Low-rank matrix approximation, an extension of CS-based sparse vector recovery theory, is then studied in this research to address the channel estimation problem of large-scale (or massive) multiuser (MU) multiple-input multiple-output (MIMO) systems. A low-rank channel matrix estimation method based on nuclear-norm regularization is formulated and solved via a dual quadratic semi-definite programming (SDP) problem. An explicit choice of the regularization parameter and useful upper bounds of the error are presented to show the efficacy of the CS method in this case. After that, both the uplink channel estimation and a downlink data recoding of massive MIMO in the interference-limited multicell scenarios are considered, where a CS-based rank-q channel approximation and multicell precoding method are proposed. The results in this work suggest that the proposed method can mitigate the effects of the pilot contamination and intercell interference, hence improves the achievable rates of the users in multicell massive MIMO systems. Finally, various low-complexity greedy techniques are then presented to confirm the efficacy and feasibility of the proposed approaches in practical applications

Compressive Sensing for Multi-channel and Large-scale MIMO Networks

Compressive sensing (CS) is a revolutionary theory that has important applications in many engineering areas. Using CS, sparse or compressible signals can be recovered from incoherent measurements with far fewer samples than the conventional Nyquist rate. In wireless communication problems where the sparsity structure of the signals and the channels can be explored and utilized, CS helps to significantly reduce the number of transmissions required to have an efficient and reliable data communication. The objective of this thesis is to study new methods of CS, both from theoretical and application perspectives, in various complex, multi-channel and large-scale wireless networks. Specifically, we explore new sparse signal and channel structures, and develop low-complexity CS-based algorithms to transmit and recover data over these networks more efficiently. Starting from the theory of sparse vector approximation based on CS, a compressive multiple-channel estimation (CMCE) method is developed to estimate multiple sparse channels simultaneously. CMCE provides a reduction in the required overhead for the estimation of multiple channels, and can be applied to estimate the composite channels of two-way relay channels (TWRCs) with sparse intersymbol interference (ISI). To improve end-to-end error performance of the networks, various iterative estimation and decoding schemes based on CS for ISI-TWRC are proposed, for both modes of cooperative relaying: Amplify-and-Forward (AF) and Decode-and-Forward (DF). Theoretical results including the Restricted Isometry Property (RIP) and low-coherent condition of the discrete pilot signaling matrix, the performance guarantees, and the convergence of the schemes are presented in this thesis. Numerical results suggest that the error performances of the system is significantly improved by the proposed CS-based methods, thanks to the awareness of the sparsity feature of the channels. Low-rank matrix approximation, an extension of CS-based sparse vector recovery theory, is then studied in this research to address the channel estimation problem of large-scale (or massive) multiuser (MU) multiple-input multiple-output (MIMO) systems. A low-rank channel matrix estimation method based on nuclear-norm regularization is formulated and solved via a dual quadratic semi-definite programming (SDP) problem. An explicit choice of the regularization parameter and useful upper bounds of the error are presented to show the efficacy of the CS method in this case. After that, both the uplink channel estimation and a downlink data precoding of massive MIMO in the interference-limited multicell scenarios are considered, where a CS-based rank-q channel approximation and multicell precoding method are proposed. The results in this work suggest that the proposed method can mitigate the effects of the pilot contamination and intercell interference, hence improves the achievable rates of the users in multicell massive MIMO systems. Finally, various low-complexity greedy techniques are then presented to confirm the efficacy and feasibility of the proposed approaches in practical applications

Estimação de canal para comunicações subaquáticas utilizando MIMO Massivo

O propósito desta dissertação de mestrado é demonstrar como a inserção das variáveis de ruído impulsivo e estimação de canal, afetam os sistemas de comunicações acústicos subaquáticos, associado à utilização de técnicas de múltiplas antenas (Multiple Input Multiple Output [MIMO]). Também tem como objetivo ilustrar como os sistemas MIMO tendem a comportar-se quando utilizam diferentes recetores, designadamente dois de baixa complexidade, o Equal Gain Combiner (EGC) e o Maximum Gain Combiner (MRC), e um de maior complexidade, o Zero Forcing. Cada um destes recetores foi analisado em diferentes cenários de ambiente, vários de níveis de correlação de canal entre antenas, e perante diferente número de antenas de transmissão e receção. Esta dissertação tende ainda a demostrar como mitigar os efeitos negativos que os sistemas MIMO apresentam relacionado com os altos níveis de correlação causado pelo insuficiente espaçamento entre as antenas, experimentado em comunicações acústicas subaquáticas